For condition-based maintenance (CBM) of machinery such as vehicles, data-driven methodologies are commonly used in an attempt to infer failure probabilities based on the current vehicle condition. For building underlying models that accurately incorporate relevant features, a significant amount of historic data on failures and machine conditions is required.
In domains wherein failures and downtime are costly, providers commonly establish replacement policies that are cautious and often overly conservative. One context that commonly gives rise to such shortcomings is a context of high-dimensional and low sample size data. Accordingly, a need exists for techniques to generate robust estimates of failure risk for high-dimensional and low sample size data.